{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:57MZOZPASNFNNAATIKF6AQEXNC","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"339f6d384be4e097a852bc635b39bd8b990012f4cfceb329afca597cfe7bda48","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-16T11:10:38Z","title_canon_sha256":"d4a6df5c5d73b349714956134304e37b236f92b3d7f1d1324d00376b7e9817dd"},"schema_version":"1.0","source":{"id":"2501.09469","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.09469","created_at":"2026-07-05T10:01:47Z"},{"alias_kind":"arxiv_version","alias_value":"2501.09469v1","created_at":"2026-07-05T10:01:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.09469","created_at":"2026-07-05T10:01:47Z"},{"alias_kind":"pith_short_12","alias_value":"57MZOZPASNFN","created_at":"2026-07-05T10:01:47Z"},{"alias_kind":"pith_short_16","alias_value":"57MZOZPASNFNNAAT","created_at":"2026-07-05T10:01:47Z"},{"alias_kind":"pith_short_8","alias_value":"57MZOZPA","created_at":"2026-07-05T10:01:47Z"}],"graph_snapshots":[{"event_id":"sha256:a7820550238ed1d5aa5f89acdc1e74c98641373a37a0c15fd5fa55e9db0bf880","target":"graph","created_at":"2026-07-05T10:01:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.09469/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this study, we firstly introduce a method that converts CityGML data into voxels which works efficiently and fast in high resolution for large scale datasets such as cities but by sacrificing some building details to overcome the limitations of previous voxelization methodologies that have been computationally intensive and inefficient at transforming large-scale urban areas into voxel representations for high resolution. Those voxelized 3D city data from multiple cities and corresponding air temperature data are used to develop a machine learning model. Before the model training, Gaussian ","authors_text":"Berk K{\\i}v{\\i}lc{\\i}m, Patrick Erik Bradley","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-16T11:10:38Z","title":"Predicting Air Temperature from Volumetric Urban Morphology with Machine Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.09469","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f2cb24146c2eea570af373c145c9318dc24744522993b64884ebdaef18c8038a","target":"record","created_at":"2026-07-05T10:01:47Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"339f6d384be4e097a852bc635b39bd8b990012f4cfceb329afca597cfe7bda48","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-16T11:10:38Z","title_canon_sha256":"d4a6df5c5d73b349714956134304e37b236f92b3d7f1d1324d00376b7e9817dd"},"schema_version":"1.0","source":{"id":"2501.09469","kind":"arxiv","version":1}},"canonical_sha256":"efd99765e0934ad68013428be040976891e00929c646415f7845a921a60cf43a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"efd99765e0934ad68013428be040976891e00929c646415f7845a921a60cf43a","first_computed_at":"2026-07-05T10:01:47.545591Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:01:47.545591Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LXrR//3XQuXB+4hSi1J8q6+0vbWWtomisTRAWcivZmMnJjhGO6Iw8gRU6tgRhbD1TsxW14bGkhZHV83hDfudCw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:01:47.546051Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.09469","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f2cb24146c2eea570af373c145c9318dc24744522993b64884ebdaef18c8038a","sha256:a7820550238ed1d5aa5f89acdc1e74c98641373a37a0c15fd5fa55e9db0bf880"],"state_sha256":"15268bac7d0a0e4bc7c024cb0d9d15081fc32cf3c287fe47e0242e6b1ab6e98c"}